English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Combining additive models and neural networks allows to broaden the scope ofstatistical regression and extend deep learning-based approaches byinterpretable structured additive predictors at the same time. Existingattempts uniting the two modeling approaches are, however, limited to veryspecific combinations and, more importantly, involve an identifiability issue.As a consequence, interpretability and stable estimation are typically lost. Wepropose a general framework to combine structured regression models and deepneural networks into a unifying network architecture. To overcome the inherentidentifiability issues between different model parts, we construct anorthogonalization cell that projects the deep neural network into theorthogonal complement of the statistical model predictor. This enables properestimation of structured model parts and thereby interpretability. Wedemonstrate the framework's efficacy in numerical experiments and illustrateits special merits in benchmarks and real-world applications.

Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities